Papers Continuous Control
“Continuous Control” 태그가 달린 논문 1,432편 · 필터 해제
Contextual Intelligence The Next Leap for Reinforcement Learning
Reinforcement learning (RL) has produced spectacular results in games, robotics, and continuous control. Yet, despite these successes, learned policies often fail to generalize beyond their training distribution, limitin…
Reinforcement LearningContinuous ControlWIMLE: Uncertainty-Aware World Models with IMLE for Sample-Efficient Continuous Control
Model-based reinforcement learning promises strong sample efficiency but often underperforms in practice due to compounding model error, unimodal world models that average over multi-modal dynamics, and overconfident pre…
Reinforcement LearningContinuous ControlUnifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics
We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planni…
Reinforcement LearningContinuous ControlAtari GamesFLAC: Maximum Entropy RL via Kinetic Energy Regularized Bridge Matching
Iterative generative policies, such as diffusion models and flow matching, offer superior expressivity for continuous control but complicate Maximum Entropy Reinforcement Learning because their action log-densities are n…
Reinforcement LearningContinuous ControlDensity EstimationTemporal Difference Learning with Constrained Initial Representations
Recently, there have been numerous attempts to enhance the sample efficiency of off-policy reinforcement learning (RL) agents when interacting with the environment, including architecture improvements and new algorithms.…
Reinforcement LearningContinuous ControlSafe Reinforcement Learning via Recovery-based Shielding with Gaussian Process Dynamics Models
Reinforcement learning (RL) is a powerful framework for optimal decision-making and control but often lacks provable guarantees for safety-critical applications. In this paper, we introduce a novel recovery-based shieldi…
Reinforcement LearningContinuous ControlCan We Really Learn One Representation to Optimize All Rewards?
As unsupervised pretraining becomes increasingly ubiquitous in reinforcement learning, a more thorough theoretical understanding of these methods becomes of equal importance to their empirical success. We focus on the se…
Representation LearningReinforcement LearningContinuous ControlConfounding Robust Continuous Control via Automatic Reward Shaping
Reward shaping has been applied widely to accelerate Reinforcement Learning (RL) agents' training. However, a principled way of designing effective reward shaping functions, especially for complex continuous control prob…
Reinforcement LearningContinuous ControlLearning in Context, Guided by Choice: A Reward-Free Paradigm for Reinforcement Learning with Transformers
In-context reinforcement learning (ICRL) leverages the in-context learning capabilities of transformer models (TMs) to efficiently generalize to unseen sequential decision-making tasks without parameter updates. However,…
Reinforcement LearningContinuous ControlMind the Gap: Rethinking I/O Design for Contact-Rich Visuomotor Policy Learning
Contact-rich teleoperation logs expose a policy I/O design choice: demonstrations may contain the robot execution (E), leader command (C), or both. These signals are not interchangeable: E2E may discard contact-generatin…
Continuous ControlZero-Shot UAV Navigation in Forests via Relightable 3D Gaussian Splatting
UAV navigation in unstructured outdoor environments using passive monocular vision is hindered by the substantial visual domain gap between simulation and reality. While 3D Gaussian Splatting enables photorealistic scene…
Reinforcement LearningContinuous ControlDistributional Reinforcement Learning with Diffusion Bridge Critics
Recent advances in diffusion-based reinforcement learning (RL) methods have demonstrated promising results in a wide range of continuous control tasks. However, existing works in this field focus on the application of di…
Reinforcement LearningContinuous ControlStepScorer: Accelerating Reinforcement Learning with Step-wise Scoring and Psychological Regret Modeling
Reinforcement learning algorithms often suffer from slow convergence due to sparse reward signals, particularly in complex environments where feedback is delayed or infrequent. This paper introduces the Psychological Reg…
Reinforcement LearningContinuous ControlRDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization
Vision-Language-Action (VLA) models hold promise for generalist robotics but currently struggle with data scarcity, architectural inefficiencies, and the inability to generalize across different hardware platforms. We in…
Continuous ControlContinuous Control of Editing Models via Adaptive-Origin Guidance
Diffusion-based editing models have emerged as a powerful tool for semantic image and video manipulation. However, existing models lack a mechanism for smoothly controlling the intensity of text-guided edits. In standard…
Continuous ControlZero-Shot Off-Policy Learning
Off-policy learning methods seek to derive an optimal policy directly from a fixed dataset of prior interactions. This objective presents significant challenges, primarily due to the inherent distributional shift and val…
Reinforcement LearningContinuous ControlRN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning
On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies, often leading to brittle optimization wh…
Reinforcement LearningContinuous ControlError Amplification Limits ANN-to-SNN Conversion in Continuous Control
Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANNs), avoiding further costly training. This property is particularly attractive…
Reinforcement LearningContinuous ControlDynamicVLA: A Vision-Language-Action Model for Dynamic Object Manipulation
Manipulating dynamic objects remains an open challenge for Vision-Language-Action (VLA) models. Although recent VLAs generalize well in static manipulation, dynamic scenes introduce a latency-induced perception-execution…
Continuous ControlCADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning
Transfer learning promises to reduce the high sample complexity of deep reinforcement learning (RL), yet existing methods struggle with domain shift between source and target environments. Policy distillation provides po…
Reinforcement LearningContinuous ControlTransfer Learning